Combining Cross-Sectional and Longitudinal Data

In the great bulk of the nationally representative looks at disparities in mental health care, in that bulk of research, we've used cross-sectional studies. So that's the main, hopefully, innovation of this episode's work is that where you're just using longitudinal data.

So, this is often the scene in claims data. You know, if you have a large insurance data set, then you can look at their claims over a long course of time and see how people go in and out of care, how they... the different kinds of care that they see, when they fill their prescriptions, if they hit the emergency room, inpatient care, more acute kind of settings.

And so what hasn't been done is sort of mixing those two things -- these nationally representative cross sections and this idea of longitudinal data where you can track people's care. Well, the MEPS, the Medical Expenditure Panel Survey, has two years of longitudinal data.

So it doesn't track people for their entire medical career or medical history or something like that, but it does have two years of dates on every visit they make, on every prescription they fill, what kind of visit it was, whether it was outpatient or inpatient or was it in an office or it has an ICD-9 code attached to their illness. It has the drug type. So you can look at their patterns of care, how they go in and out of acute care, whether they're seeing a primary care provider or a specialist, and track that over two years.

And that's, I think, a real advantage over the kind of Medicaid claims because this is a nationally representative data set and then an advantage over other cross-sectional data sets because you can actually kind of see over time why individuals are initiating care, why they're dropping out of care, if there are gaps in their care. You can see that a lot easier, I think, with these episodes of care.

And so we thought that that would be an improvement for policymakers, to get that kind of information. In the past, we have said that there are disparities in our country in accessing mental health care, and they're large. They're two to one, for example, between whites and African-Americans and whites and Latinos.

But what we've done is looked at a year worth of mental health visits more and seeing how many there are, or if there was any over the course of that year. And we aren't able to kind of tease out how long those episodes of care are.

Now, if non-Latino whites have very long episodes of care, they're more likely to kind of wedge into that cross section that we're looking at in the cross-sectional data. And so there might be kind of a confounding of that any mental health care in the last year due to the fact that whites have longer episodes of care. So we don't know whether they're initiating more care or whether their episodes are just longer or what it is about their mental health care that's causing these large disparities that we see in that cross section.

And so we're hoping that that longitudinal data will be able to tease some of that out, and then we can make some recommendations to policymakers for what are the correlates of initiation of care and what are the correlates of dropout from care.